In electricity distribution systems, loss occurs when current flows through the conductors in the system. This energy loss through a conductor may be calculated according to I2R, where I is the current through conductor whose resistance is R. The net demand or current flow also depends on the voltage profile on the feeders. Reactive compensation can reduce unnecessary current flows and in turn reduce losses. Voltage regulation affects the effective loading of feeders due to the voltage dependence characteristics of the loads, as well as the energy losses.
Voltage and Var optimization (VVO) systems are employed in electricity distribution systems to optimize the distribution of voltages and currents on distribution systems. VVO systems endeavor to maximize efficiency (minimize MW loss minimization or MW demand) of energy delivery by controlling voltage regulators (Voltage) and reactive power resources (Var) by employing online system models and demand forecasts.
With reference to FIG. 1, an electricity distribution network is shown. As can be seen, a substation provides power to a plurality of loads through the substation transformers, feeders, and laterals. Distributed at various points in the distribution network are capacitor banks C that may be fixed or switched, and voltage regulators that can be locally or remotely controlled to alter the tap settings. The connectivity of the network and the status of the various equipment, such as transformers, loads, capacitors, voltage regulators, are monitored via sensors and a communication infrastructure. Monitored data may include voltage, current and/or power at or through various points or conductors. This information is transmitted to a distribution management system (DMS) or a substation automation system (SAS). Upon receiving the updated status information, the system model (load flow model) within the DMS is updated. A load forecast is performed based on the SCADA data, customer billing data, and/or data collected from advanced metering infrastructure (AMI). The VVO, based on the load forecasts, the system model, and the available control information, then determines the best tap settings for the voltage regulators and on load tap change (OLTC) transformers, and the Var resources such as switched shunt capacitors or reactors. Control commands are then transmitted back to the various elements in the distribution grid where the control actions are carried out, bring the system to a more efficient operating state.
VVO is the decision making process that analyzes the input data from the field and generates the control signals to be transmitted to the controllers in the filed. Voltage regulation optimization (VRO) is a subsystem of a VVO system. The VRO may work stand alone or in conjunction with a Var optimization (VARO) to provide integrated VVO solutions.
The concept of demand reduction on electric distribution systems dates back several decades and many in the industry and the research communities have attempted to develop effective solution methodology and processes. The common practice of energy conservation voltage reduction (ECVR) attempts to reduce demand on distribution systems by lowering the voltage on the feeders as as much as service agreement allows. This approach is not very effective since the underlying assumption is end user loads decrease as voltages decrease, which is only partially true. In reality, some loads are like constant impedances whose loads decreases when voltage decreases. Some loads behave like constant power loads that remain constant regardless voltage decrease or increase. When voltages are reduced on a feeder that has predominantly constant power loads, the effect of voltage reduction is increased net demand, since to maintain constant power, the loads will draw more currents which increase loss on top of the constant power. Systematic approaches are needed to take into consideration the effect of the voltage on the loads when determining if voltages should be increased or reduced and by how much at different locations on the distribution network. This is a difficult integer nonlinear optimization problem with tough constraints. Most solution approaches proposed to date are applicable to small, very simplified academic models, and are not suitable for large scale, meshed, multi-source, multi-phase unbalanced distribution systems that are common in real distribution networks. The deficiencies in conventional methods are due to (1) the model being too simplified to represent a real system, by assuming radial topology, balanced construction and operation, or ignoring the effect of transformer connections (for example, wye to delta connections), (2) the computation efficiency being so low that the solution can not be scaled for either online or offline applications for large system, or (3) the methods are not general enough and have limited optimizing capability.
Thus, there is a need in the art for an optimization solution applicable to large scale, meshed, multi-source, multi-phase unbalanced distribution systems.